Machine Learning to Identify Dynamic Properties of Railway Track Components

Abstract

Investigating the condition of rail track components is important for track maintenance and developing a greater understanding of track design. Railway inspection can be destructive and non-destructive approaches. In the railway industry, the non-destructive approaches are preferred because they retain the track in operation thus significantly reducing the cost of fault testing. One of the non-destructive approaches is using machine learning which is applied in this study. Field measurements and advanced analysis of results are used to extract track properties. This study creates, tunes and examines the validity of different machine learning techniques. The aim is to extract the dynamic track properties from the in-field measurements without needing the intermediary steps, saving both time and effort. Contributions of this study demonstrate that machine learning techniques have the potential to save cost and time for railway inspection. Moreover, the accuracy is satisfied. The following models are produced: Linear Regression, K-Nearest Neighbors, Gradient Boosting and a Convolutional Neural Network. We observe the limitations of linear regression and tune the remainder, producing three models with low errors.